MétaCan
Menu
Back to cohort
Record W3126028443

The Legacy of Clytemnestra in Homer’s Odyssey

2020· article· en· W3126028443 on OpenAlexaffabout
Leanne Buttery

Bibliographic record

VenueStudent Research Proceedings · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicClassical Antiquity Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsWifeHEROEPICLiteratureHistorySociologyLawPhilosophyTheologyArtPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In Homer’s epic, The Odyssey, the author places Clytemnestra in stark opposition to Penelope, the wife of the epic’s hero, Odysseus. Clytemnestra, the wife of King Agamemnon, cheated on her husband and killed him upon his return from the Trojan war; an action that placed her in the category of a ‘bad wife.’ In contrast, Penelope uses her autonomy to stay within the traditional social roles of a good Greek wife. Penelope is compared with Clytemnestra and found equal to her, yet above her in morality – for she never betrays Odysseus. Even though Penelope does not act like Clytemnestra, the consequences of Clytemnestra’s action damage the reputation of not only Penelope but of all women. Despite his trust in Penelope, Odysseus treats her with suspicion until the end of the epic – as if she too may betray him. This paper will explain how the legacy of Clytemnestra’s actions impacted Penelope throughout the rest of the epic. In order to fully contextualize the power of Clytemnestra’s actions, this paper will analyze how the literary representation of women in classical works expressed the belief that women by nature behaved like Clytemnestra. Regardless of the faithfulness of Penelope, she remains under the cloud of a bad wife because all women – even good ones – cannot be trusted. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Benjamin Garstad Department: History

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.279
GPT teacher head0.506
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueStudent Research ProceedingsSame topicClassical Antiquity StudiesFrench-language works237,207